Project Summary In this project, we propose to validate and characterize ?ber orientation estimation from diffusion tensor imag- ing (DTI) through the optimization of a multi-modality, multi-scale imaging pipeline for whole mouse brains. DTI is a powerful tool used to noninvasively report 3D microstructural properties of nervous tissue on a macroscopic scale, and has played an important role in the understanding and diagnosis of a number of neurological disease processes. Modern acquisitions of DTI data can be processed to generate a 3D diffusion pro?le known as an orientation diffusion function (ODF) at each voxel. The ODF is used to infer the orientation of local axon ?ber pop- ulations. Previous efforts to validate these orientation estimates have primarily relied on serial optical histology as a ground truth dataset. Histology-based pipelines involve the labor intensive task of physically sectioning the tissue into thin slices, leading to physical destruction of the sample and anisotropic resolution. These limitations potentially confound the accuracy of 3D orientation estimation, complicate the process of spatially registering the ground-truth and DTI datasets, and limit quantitative comparisons to select regions of interest (ROI) across the brain sample. In recent years, synchrotron x-ray microcomputed tomography (microCT) has emerged as a powerful tool for high-resolution tissue imaging. With a mosaic projection-stitching method, a whole mouse brain can be imaged at an isotropic, 3D resolution of 1.2 microns after prior imaging with DTI. To enhance microCT contrast, the tis- sue specimens are ?xed and stained with the same kind of metal-based stains used in electron microscopy (EM) prior to embedding in resin. We will optimize this microCT-EM validation pipeline to address the limitations of pre- vious histology-based studies, and characterize DTI algorithm performance across a whole mouse brain using micron- to nano-scale neurological information. The speci?c aims of the proposal are: (1) model phase contrast to optimize microCT data acquisition, (2) vali- date DTI ODF reconstruction methods using ground-truth microCT (3) characterize DTI performance using under- lying tissue microstructure information from EM. Upon completion, aim 1 will generate a novel theoretical model and acquisition strategy to exploit microCT phase contrast in strongly absorbing biological samples. Aim 2 will generate a ground-truth dataset of ODFs across a whole mouse brain, which will be used to calculate algorithm- speci?c spatial maps of DTI performance. In Aim 3, around 20 ROI will be selected for nano-scale imaging with EM, and DTI performance will be characterized by quantitative features of the underlying neural architecture. These results will provide an unprecedented microstructure-driven understanding of the DTI signal, allowing fu- ture studies to develop more advanced DTI models and acquisition strategies to better leverage ?ber orientation and connectivity information in the treatment of neurological disease.